Unless otherwise indicated herein, the approaches described in this section are not prior art to the claims in this application and are not admitted to be prior art by inclusion in this section.
As the volume and complexity of data stored in data warehouses continues to increase, exploration of that data for analytical purposes may be hindered. Recommender systems (RS) have grown very popular on the internet with sites like AMAZON™, NETFLIX™, and others. These systems help users explore available content related to what they are currently viewing.
Recent systems consider the use of RS techniques to suggest data warehouse queries to aid an analyst in pursuing his or her explorations. However, there is a need for even more selective approaches to creating queries for data warehouses.
The present disclosure addresses this and other issues with systems and methods allowing personalized multi-dimensional query expansion using semantics, user profiles, and/or usage statistics.